58 research outputs found
Energy Harvesting Wireless Communications: A Review of Recent Advances
This article summarizes recent contributions in the broad area of energy
harvesting wireless communications. In particular, we provide the current state
of the art for wireless networks composed of energy harvesting nodes, starting
from the information-theoretic performance limits to transmission scheduling
policies and resource allocation, medium access and networking issues. The
emerging related area of energy transfer for self-sustaining energy harvesting
wireless networks is considered in detail covering both energy cooperation
aspects and simultaneous energy and information transfer. Various potential
models with energy harvesting nodes at different network scales are reviewed as
well as models for energy consumption at the nodes.Comment: To appear in the IEEE Journal of Selected Areas in Communications
(Special Issue: Wireless Communications Powered by Energy Harvesting and
Wireless Energy Transfer
Two-timescale resource allocation for automated networks in IIoT
The rapid technological advances of cellular technologies will revolutionize network automation in industrial internet of things (IIoT). In this paper, we investigate the two-timescale resource allocation problem in IIoT networks with hybrid energy supply, where temporal variations of energy harvesting (EH), electricity price, channel state, and data arrival exhibit different granularity. The formulated problem consists of energy management at a large timescale, as well as rate control, channel selection, and power allocation at a small timescale. To address this challenge, we develop an online solution to guarantee bounded performance deviation with only causal information. Specifically, Lyapunov optimization is leveraged to transform the long-term stochastic optimization problem into a series of short-term deterministic optimization problems. Then, a low-complexity rate control algorithm is developed based on alternating direction method of multipliers (ADMM), which accelerates the convergence speed via the decomposition-coordination approach. Next, the joint channel selection and power allocation problem is transformed into a one-to-many matching problem, and solved by the proposed price-based matching with quota restriction. Finally, the proposed algorithm is verified through simulations under various system configurations
Power control optimization for large-scale multi-antenna systems
Large-scale multi-antenna systems can effectively improve data transmission reliability and throughput for smart grid. However, the massive number of antennas and radio frequency (RF) chains also result in high complexity and energy cost. In this paper, we develop a new performance benchmark named energy economic efficiency for measuring the time-average throughput per energy cost. Then, we investigate how to maximize long-term energy economic efficiency via the joint optimization of communication and energy resource allocation. The formulated joint optimization problem is NP-hard because it not only involves long-term nonlinear optimization objective and constraints, but also involves both integer and continuous optimization variables. Next, we propose an online joint antenna selection and power control algorithm by combining nonlinear fractional programming, Lyapunov optimization, and bisection method. The proposed algorithm can achieve bounded performance deviation from the optimum performance without requiring the prior knowledge of future channel state information (CSI), energy arrival, and electricity price. Finally, a comprehensive theoretical analysis is provided, and the proposed algorithm is verified through simulations under various system configurations
The Value-of-Information in Matching with Queues
We consider the problem of \emph{optimal matching with queues} in dynamic
systems and investigate the value-of-information. In such systems, the
operators match tasks and resources stored in queues, with the objective of
maximizing the system utility of the matching reward profile, minus the average
matching cost. This problem appears in many practical systems and the main
challenges are the no-underflow constraints, and the lack of matching-reward
information and system dynamics statistics. We develop two online matching
algorithms: Learning-aided Reward optimAl Matching () and
Dual- () to effectively resolve both challenges.
Both algorithms are equipped with a learning module for estimating the
matching-reward information, while incorporates an additional
module for learning the system dynamics. We show that both algorithms achieve
an close-to-optimal utility performance for any
, while achieves a faster convergence speed and a
better delay compared to , i.e., delay and convergence under
compared to delay and convergence under
( and are maximum estimation errors for
reward and system dynamics). Our results reveal that information of different
system components can play very different roles in algorithm performance and
provide a systematic way for designing joint learning-control algorithms for
dynamic systems
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